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Description

In the multiple instance learning setting, each observation is a bag of feature vectors
of which one or more vectors indicates membership in a class. The primary
task is to identify if any vectors in the bag indicate class membership while ignoring
vectors that do not. We describe here a kernel-based technique that defines
a parametric family of kernels via conformal transformations and jointly learns
a discriminant function over bags together with the optimal parameter settings of
the kernel. Learning a conformal transformation effectively amounts to weighting
regions in the feature space according to their contribution to classification accuracy;
regions that are discriminative will be weighted higher than regions that are
not. This allows the classifier to focus on regions contributing to classification
accuracy while ignoring regions that correspond to vectors found both in positive
and in negative bags. We show how parameters of this transformation can
be learned for support vector machines by posing the problem as a multiple kernel
learning problem. The resulting multiple instance classifier gives competitive
accuracy for several multi-instance benchmark datasets from different domains.